If you run a purely brick-and-mortar operation, you might be feeling the pressure of online competitors eating away at your potential sales volume.
On the other hand, you may have decided to make the leap into the online world in tandem with a grounded physical presence. If that’s the case, you might have found that it’s hard to handle the increased sales volume that comes with exposing your products to the online world.
Either way, if you haven’t considered it yet, you’re likely going to want to look into demand forecasting.
What Is Demand Forecasting?
Demand forecasting is the act of using expert opinions, analytical data, and other factors to make a prediction about future customer demand for a company’s products and services. It is a crucial element in helping enterprises avoid things like:
- Accidentally overstocking products, leading to dead stock.
- Understocking products, leading to product shortages and missed sales opportunities.
- Paying for excess warehouse space.
- Knowing when to hire or let go of extra staff for seasonal or trending shifts in demand.
- Misappropriating resources, in general, in ways that can be easily avoided by simple analysis.
Demand Forecasting Methods
There are numerous demand forecasting methods. This is only natural, as it’s impossible to apply the same forecasting formula to every business or scenario. A ten-year-old company selling cutting edge technological products from its manufacturing base in Germany is going to have a very different forecasting process than, say, a brand new car dealership in Erie, Pennsylvania.
While the various approaches to demand forecasting are legion, we’ve gone ahead and lumped the bulk of the methods into two categories: survey methods and statistical methods.
The first umbrella term for demand forecasting is using a “survey method.” This typically means you’ll be looking for quality information rather than quantities of information.
Many survey methods of demand forecasting tend to be particularly helpful for startups or brand new, innovative products where there is little-to-no past statistical data available to analyze.
In essence, qualitative forecasting consists of finding expert pieces of information to help make a prediction. Typically this comes from:
- Professionals within your industry.
- Comparative analytics.
- Other forms of already available market research.
The Delphi Method:
This method combines the powers of expert opinion and crowdsourcing. The Delphi method asks a group of experts a series of questions that they answer anonymously. The questioning goes through several rounds, during which the panel is allowed to continue to anonymously change their answers based on the others’ unidentified opinions. Over time, the opinions are clarified and specified until a general consensus is clearly defined.
Market Experiments Method:
In this method, demand is forecasted by executing various experiments within the market itself. It can utilize things like consumer surveys as well as identifying trends in consumer behavior.
Of course, nothing quite beats the power of cold, hard facts. Analytics gathered by programs like Fishbowl’s Forecast module can enable a company to use the second umbrella term, “statistical methods,” to gauge their future demands.
This method uses data from the past to predict seasonal ebbs and flows, identify sales trends, and understand cyclical patterns.
As the name implies, this method simply uses the data from the past year of a company in order to predict demands. It is dependent on the assumption that the market will remain stable.
This nuanced method is a bit more complex and can vary dramatically. It involves forecasting based on assembling various data that is relevant to a particular company. It can include things like:
- Marketing and sales data
- Competitor information.
- Social factors.
This method takes into account current business trends and factors that can impact the economic landscape. If ground is about to be broken on a new sports stadium, for instance, certain building materials will likely be in higher demand within that geographic area for a period of time.
This method is much like a smaller version of the time-series analysis method. It is meant to identify patterns and cycles within short periods of time.
Trends vs. Seasons
When it comes to forecasting fluctuations, there are two major categories: trends and seasons. Both differ and can impact your inventory forecasting, in particular, depending on how well-prepared you are.
Seasonal forecasting tends to fall under repetitive cycles that are predictable and can be easily anticipated. Christmas, Thanksgiving, and summer weather are all classic forces behind seasonal forecasting.
Trends, on the other hand, often require more complex data sets that are tailored towards discovering unknown yet permanent shifts in future inventory requirements. While seasonal demand is easy to identify, trends are a bit of a subtler art that looks for emerging behavior rather than that which has been established in the past.
The modern e-commerce world has dramatically changed the landscape of demand forecasting. It has allowed businesses, great and small, to collect valuable data that wasn’t readily available in the past. This data comes in a variety of ways, especially for companies that have embraced the diverse strategy of an omnichannel shopping experience.
When properly integrated across a business’s operations, this data can then be analyzed with cutting-edge software and used to optimize a company’s efforts based on what is selling when.
All of this gives the modern, 21st-century company an incredible level of insight and control over its own future. Demand forecasting and the data and analysis that backs it up have provided the capability to predict and prepare for the fluctuations and demands of the future.